Skip to main content
Glama
ComplianceCow

ComplianceCow MCP Server

suggest_control_citations

Suggest control citations by matching control names or descriptions to relevant compliance regulations, helping attach appropriate citations to assessment controls.

Instructions

Suggest control citations for a given control name or description.

WORKFLOW: When user provides a requirement, ask which assessment they want to use. Get assessment name from user, then resolve to assessmentId (mandatory). For control: offer two options - select from existing control on selected assessment OR create new control. If selecting existing control, get control name from user and resolve to controlId. If creating new control, controlId will be empty.

This function provides suggestions for control citations based on control names or descriptions. The user can select from the suggested controls to attach citations to their assessment controls.

Args: controlName (str): Name of control to get suggestions for (required). assessmentId (str): Assessment ID - resolved from assessment name (required). description (str, optional): Description of the control to get suggestions for. controlId (str, optional): Control ID - resolved from control name if selecting existing control, empty if creating new control.

Returns: Dict with success status and suggestions: - success (bool): Whether the request was successful - items (List[dict]): List of suggestion items, each containing: - inputControlName (str): The input control name - controlId (str): The control ID (empty if control doesn't exist yet) - suggestions (List[dict]): List of suggested controls, each containing: - Name (str): Control name - Control ID (int): Control ID number - Control Classification (str): Classification type - Impact Zone (str): Impact zone category - Control Requirement (str): Requirement level - Sort ID (str): Sort identifier - Control Type (str): Type of control - Score (float): Similarity score - authorityDocument (str): Name of the authorityDocument - error (str, optional): Error message if request failed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
controlNameYes
descriptionYes
controlIdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It explains the suggestion generation process and return structure, but could explicitly state that it is a read-only operation with no side effects. The mention of 'suggestions' implies non-mutating behavior, but direct declaration would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (WORKFLOW, Args, Returns). It is detailed but not excessively verbose. Minor redundancy exists (e.g., repeating the suggestion context), but overall it efficiently conveys the necessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description covers workflow, parameters, and return schema adequately. However, the omission of assessmentId from the input schema is a significant gap that undermines completeness. Error handling and permissions are not mentioned, but with output schema present, return values are sufficiently documented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It explains controlName and controlId well, but mentions assessmentId as mandatory despite it not being in the input schema. This inconsistency reduces clarity. The description adds value by describing the optional description parameter and the workflow, but the gap regarding assessmentId is notable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Suggest control citations for a given control name or description.' It also explains the workflow, differentiating it from sibling tools like fetch_controls by focusing on suggestions based on input. The verb 'suggest' and resource 'control citations' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit workflow guidance: 'When user provides a requirement, ask which assessment they want to use...' It details prerequisites (assessmentId resolution), two options (select existing or create new), and the role of each parameter. This fully informs when and how to use the tool vs alternatives like fetch_controls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ComplianceCow/cow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server